{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T13:20:46Z","timestamp":1790083246050,"version":"4.0.1"},"reference-count":44,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2023,4,13]],"date-time":"2023-04-13T00:00:00Z","timestamp":1681344000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","award":["2021YJS033"],"award-info":[{"award-number":["2021YJS033"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2023,6,30]]},"abstract":"<jats:p>In intelligent logistics systems, predicting the Estimated Time of Pick-up Arrival (ETPA) of packages is a crucial task, which aims to predict the courier\u2019s arrival time to all the unpicked-up packages at any time. Accurate prediction of ETPA can help systems alleviate customers\u2019 waiting anxiety and improve their experience. We identify three main challenges of this problem. First, unlike the travel time estimation problem in other fields like ride-hailing, the ETPA task is distinctively a multi-destination and path-free prediction problem. Second, an intuitive idea for solving ETPA is to predict the pick-up route and then the time in two stages. However, it is difficult to accurately and efficiently predict couriers\u2019 future routes in the route prediction step since their behaviors are affected by multiple complex factors. Third, furthermore, in the time prediction step, the requirement for providing a courier\u2019s all unpicked-up packages\u2019 ETPA at once in real time makes the problem even more challenging. To tackle the preceding challenges, we propose RankETPA, which integrates the route inference into the ETPA prediction. First, a learning-based pick-up route predictor is designed to learn the route-ranking strategies of couriers from their massive spatial-temporal behaviors. Then, a spatial-temporal attention-based arrival time predictor is designed for real-time ETPA inference via capturing the spatial-temporal correlations between the unpicked-up packages. Extensive experiments on two real-world datasets and a synthetic dataset demonstrate that RankETPA achieves significant performance improvement against the baseline models.<\/jats:p>","DOI":"10.1145\/3582561","type":"journal-article","created":{"date-parts":[[2023,2,8]],"date-time":"2023-02-08T13:28:10Z","timestamp":1675862890000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["Enough Waiting for the Couriers: Learning to Estimate Package Pick-up Arrival Time from Couriers\u2019 Spatial-Temporal Behaviors"],"prefix":"10.1145","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6130-126X","authenticated-orcid":false,"given":"Haomin","family":"Wen","sequence":"first","affiliation":[{"name":"Beijing Jiaotong University, China and Beijing Key Laboratory of Traffic Data Analysis and Mining, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1611-4323","authenticated-orcid":false,"given":"Youfang","family":"Lin","sequence":"additional","affiliation":[{"name":"Beijing Jiaotong University, China and Beijing Key Laboratory of Traffic Data Analysis and Mining, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8869-186X","authenticated-orcid":false,"given":"Fan","family":"Wu","sequence":"additional","affiliation":[{"name":"Cainiao Network, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0501-9363","authenticated-orcid":false,"given":"Huaiyu","family":"Wan","sequence":"additional","affiliation":[{"name":"Beijing Jiaotong University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6109-4704","authenticated-orcid":false,"given":"Zhongxiang","family":"Sun","sequence":"additional","affiliation":[{"name":"Beijing Jiaotong University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4008-4718","authenticated-orcid":false,"given":"Tianyue","family":"Cai","sequence":"additional","affiliation":[{"name":"Beijing Jiaotong University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8038-7636","authenticated-orcid":false,"given":"Hongyu","family":"Liu","sequence":"additional","affiliation":[{"name":"Beijing Jiaotong University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3008-4511","authenticated-orcid":false,"given":"Shengnan","family":"Guo","sequence":"additional","affiliation":[{"name":"Beijing Jiaotong University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0636-3905","authenticated-orcid":false,"given":"Jianbin","family":"Zheng","sequence":"additional","affiliation":[{"name":"Cainiao Network, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7379-5129","authenticated-orcid":false,"given":"Chao","family":"Song","sequence":"additional","affiliation":[{"name":"Cainiao Network, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1863-2316","authenticated-orcid":false,"given":"Lixia","family":"Wu","sequence":"additional","affiliation":[{"name":"Cainiao Network, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,4,13]]},"reference":[{"key":"e_1_3_2_2_2","volume-title":"Proceedings of the Workshop on Negative Dependence in Machine Learning","author":"Bello Irwan","year":"2019","unstructured":"Irwan Bello, Sayali Kulkarni, Sagar Jain, Craig Boutilier, Ed Chi, Elad Eban, Xiyang Luo, Alan Mackey, and Ofer Meshi. 2019. Seq2Slate: Re-ranking and slate optimization with RNNs. In Proceedings of the Workshop on Negative Dependence in Machine Learning."},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1287\/opre.2018.1784"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.3390\/rs70303347"},{"key":"e_1_3_2_5_2","article-title":"CrowdExpress: A probabilistic framework for on-time crowdsourced package deliveries","author":"Chen Chao","year":"2020","unstructured":"Chao Chen, Sen Yang, Yasha Wang, Bin Guo, and Daqing Zhang. 2020. CrowdExpress: A probabilistic framework for on-time crowdsourced package deliveries. IEEE Transactions on Big Data 8, 3 (2020), 827\u2013842.","journal-title":"IEEE Transactions on Big Data"},{"issue":"6","key":"e_1_3_2_6_2","first-page":"1478","article-title":"crowddeliver: Planning city-wide package delivery paths leveraging the crowd of taxis","volume":"18","author":"Chen Chao","year":"2016","unstructured":"Chao Chen, Daqing Zhang, Xiaojuan Ma, Bin Guo, Leye Wang, Yasha Wang, and Edwin Sha. 2016. crowddeliver: Planning city-wide package delivery paths leveraging the crowd of taxis. IEEE Transactions on Intelligent Transportation Systems 18, 6 (2016), 1478\u20131496.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1179"},{"key":"e_1_3_2_8_2","article-title":"End-to-end prediction of parcel delivery time with deep learning for smart-city applications","author":"Araujo Arthur Cruz de","year":"2021","unstructured":"Arthur Cruz de Araujo and Ali Etemad. 2021. End-to-end prediction of parcel delivery time with deep learning for smart-city applications. IEEE Internet of Things Journal 8, 23 (2021), 17043\u201317056.","journal-title":"IEEE Internet of Things Journal"},{"key":"e_1_3_2_9_2","first-page":"197","volume-title":"Proceedings of the 2008 11th International IEEE Conference on Intelligent Transportation Systems","author":"Fabritiis Corrado De","year":"2008","unstructured":"Corrado De Fabritiis, Roberto Ragona, and Gaetano Valenti. 2008. Traffic estimation and prediction based on real time floating car data. In Proceedings of the 2008 11th International IEEE Conference on Intelligent Transportation Systems. 197\u2013203."},{"key":"e_1_3_2_10_2","first-page":"3337","volume-title":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Fu Kun","year":"2020","unstructured":"Kun Fu, Fanlin Meng, Jieping Ye, and Zheng Wang. 2020. CompactETA: A fast inference system for travel time prediction. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 3337\u20133345."},{"key":"e_1_3_2_11_2","volume-title":"New Ideas in Optimization","author":"Gambardella Luca Maria","year":"1999","unstructured":"Luca Maria Gambardella, \u00c9ric Taillard, and Giovanni Agazzi. 1999. MACS-VRPTW: A multiple colony system for vehicle routing problems with time windows. In New Ideas in Optimization, D. Corne, M. Dorigo, and F. Glover (Eds.). McGraw-Hill, London, UK, 63\u201376."},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467068"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1162\/089976600300015015"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1504\/IJLSM.2017.087786"},{"key":"e_1_3_2_15_2","first-page":"376","volume-title":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics","author":"Guo Junliang","year":"2020","unstructured":"Junliang Guo, Linli Xu, and Enhong Chen. 2020. Jointly masked sequence-to-sequence model for non-autoregressive neural machine translation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 376\u2013385."},{"key":"e_1_3_2_16_2","first-page":"583","volume-title":"Proceedings of the 2018 Conference on Natural Language Learning","author":"He Xuanli","year":"2018","unstructured":"Xuanli He, Gholamreza Haffari, and Mohammad Norouzi. 2018. Sequence to sequence mixture model for diverse machine translation. In Proceedings of the 2018 Conference on Natural Language Learning. 583\u2013592."},{"key":"e_1_3_2_17_2","unstructured":"Google OR-Tools. n.d. Vehicle Routing Problem. Retrieved February 17 2023 from https:\/\/developers.google.com\/optimization\/routing\/vrp."},{"key":"e_1_3_2_18_2","first-page":"1417","volume-title":"Proceedings of the 2020 IEEE 36th International Conference on Data Engineering","author":"Hu Jilin","year":"2020","unstructured":"Jilin Hu, Bin Yang, Chenjuan Guo, Christian S. Jensen, and Hui Xiong. 2020. Stochastic origin-destination matrix forecasting using dual-stage graph convolutional, recurrent neural networks. In Proceedings of the 2020 IEEE 36th International Conference on Data Engineering. 1417\u20131428."},{"key":"e_1_3_2_19_2","first-page":"448","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Ioffe Sergey","year":"2015","unstructured":"Sergey Ioffe and Christian Szegedy. 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In Proceedings of the International Conference on Machine Learning. 448\u2013456."},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trb.2013.03.008"},{"key":"e_1_3_2_21_2","article-title":"A unified neural network approach for estimating travel time and distance for a taxi trip","author":"Jindal Ishan","year":"2017","unstructured":"Ishan Jindal, Tony (Zhiwei) Qin, Xuewen Chen, Matthew Nokleby, and Jieping Ye. 2017. A unified neural network approach for estimating travel time and distance for a taxi trip. arXiv preprint arXiv:1710.04350 (2017).","journal-title":"arXiv preprint arXiv:1710.04350"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/30.1-2.81"},{"key":"e_1_3_2_23_2","volume-title":"Proceedings of the 7th International Conference on Learning Representations","author":"Kool Wouter","year":"2019","unstructured":"Wouter Kool, Herke van Hoof, and Max Welling. 2019. Attention, learn to solve routing problems! In Proceedings of the 7th International Conference on Learning Representations."},{"key":"e_1_3_2_24_2","first-page":"521","volume-title":"Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","author":"Kurata Gakuto","year":"2016","unstructured":"Gakuto Kurata, Bing Xiang, and Bowen Zhou. 2016. Improved neural network-based multi-label classification with better initialization leveraging label co-occurrence. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 521\u2013526."},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0377-2217(02)00486-1"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220033"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2015.10.002"},{"key":"e_1_3_2_28_2","article-title":"Neural machine translation and sequence-to-sequence models: A tutorial","author":"Neubig Graham","year":"2017","unstructured":"Graham Neubig. 2017. Neural machine translation and sequence-to-sequence models: A tutorial. arXiv preprint arXiv:1703.01619 (2017).","journal-title":"arXiv preprint arXiv:1703.01619"},{"issue":"4","key":"e_1_3_2_29_2","first-page":"2659","article-title":"Nei-TTE: Intelligent traffic time estimation based on fine-grained time derivation of road segments for smart city","volume":"16","author":"Qiu Jing","year":"2019","unstructured":"Jing Qiu, Lei Du, Dongwen Zhang, Shen Su, and Zhihong Tian. 2019. Nei-TTE: Intelligent traffic time estimation based on fine-grained time derivation of road segments for smart city. IEEE Transactions on Industrial Informatics 16, 4 (2019), 2659\u20132666.","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"e_1_3_2_30_2","doi-asserted-by":"crossref","unstructured":"Suttinee Sawadsitang Dusit Niyato Kongrath Suankaewmanee and Puay Siew Tan. 2019. Re-route package pickup and delivery planning with random demands. In Proceedings of the 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall) .","DOI":"10.1109\/VTCFall.2019.8891547"},{"key":"e_1_3_2_31_2","article-title":"Travel time estimation using floating car data","author":"Sevlian Raffi","year":"2010","unstructured":"Raffi Sevlian and Ram Rajagopal. 2010. Travel time estimation using floating car data. arXiv preprint arXiv:1012.4249 (2010).","journal-title":"arXiv preprint arXiv:1012.4249"},{"key":"e_1_3_2_32_2","first-page":"1","volume-title":"Proceedings of the 2018 International Conference on Bangla Speech and Language Processing","author":"Sharfuddin Abdullah Aziz","year":"2018","unstructured":"Abdullah Aziz Sharfuddin, Md. Nafis Tihami, and Md. Saiful Islam. 2018. A deep recurrent neural network with BiLSTM model for sentiment classification. In Proceedings of the 2018 International Conference on Bangla Speech and Language Processing. 1\u20134."},{"key":"e_1_3_2_33_2","first-page":"71","volume-title":"Proceedings of the 2021 4th International Conference on Information Management and Management Science","author":"Sun Yaqiang","year":"2021","unstructured":"Yaqiang Sun and Ailing Chen. 2021. Algorithm design for solving VRPTW problem in supermarket chain distribution. In Proceedings of the 2021 4th International Conference on Information Management and Management Science. 71\u201375."},{"key":"e_1_3_2_34_2","unstructured":"Ilya Sutskever Oriol Vinyals and Quoc V. Le. 2014. Sequence to sequence learning with neural networks. In Proceedings of the 27th International Conference on Neural Information Processing Systems (NIPS\u201914) . 3104\u20133112."},{"key":"e_1_3_2_35_2","first-page":"5998","volume-title":"Advances in Neural Information Processing Systems 30","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems 30. 5998\u20136008."},{"key":"e_1_3_2_36_2","first-page":"2692","volume-title":"Advances in Neural Information Processing Systems 28","author":"Vinyals Oriol","year":"2015","unstructured":"Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly. 2015. Pointer networks. In Advances in Neural Information Processing Systems 28. 2692\u20132700."},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11877"},{"issue":"2","key":"e_1_3_2_38_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3293317","article-title":"A simple baseline for travel time estimation using large-scale trip data","volume":"10","author":"Wang Hongjian","year":"2019","unstructured":"Hongjian Wang, Xianfeng Tang, Yu-Hsuan Kuo, Daniel Kifer, and Zhenhui Li. 2019. A simple baseline for travel time estimation using large-scale trip data. ACM Transactions on Intelligent Systems and Technology 10, 2 (2019), 1\u201322.","journal-title":"ACM Transactions on Intelligent Systems and Technology"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219900"},{"key":"e_1_3_2_40_2","first-page":"774","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Wu Fan","year":"2019","unstructured":"Fan Wu and Lixia Wu. 2019. DeepETA: A spatial-temporal sequential neural network model for estimating time of arrival in package delivery system. In Proceedings of the AAAI Conference on Artificial Intelligence. 774\u2013781."},{"key":"e_1_3_2_41_2","article-title":"Deep transformer models for time series forecasting: The influenza prevalence case","author":"Wu Neo","year":"2020","unstructured":"Neo Wu, Bradley Green, Xue Ben, and Shawn O\u2019Banion. 2020. Deep transformer models for time series forecasting: The influenza prevalence case. arXiv preprint arXiv:2001.08317 (2020).","journal-title":"arXiv preprint arXiv:2001.08317"},{"key":"e_1_3_2_42_2","first-page":"2048","volume-title":"Proceedings of the 32nd International Conference on Machine Learning (ICML\u201915)","author":"Xu Kelvin","year":"2015","unstructured":"Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron C. Courville, Ruslan Salakhutdinov, Richard S. Zemel, and Yoshua Bengio. 2015. Show, attend and tell: Neural image caption generation with visual attention. In Proceedings of the 32nd International Conference on Machine Learning (ICML\u201915). 2048\u20132057."},{"key":"e_1_3_2_43_2","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1007\/978-3-319-70096-0_23","volume-title":"Proceedings of the International Conference on Neural Information Processing","author":"Yamanaka Jin","year":"2017","unstructured":"Jin Yamanaka, Shigesumi Kuwashima, and Takio Kurita. 2017. Fast and accurate image super resolution by deep CNN with skip connection and network in network. In Proceedings of the International Conference on Neural Information Processing. 217\u2013225."},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.113151"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403307"}],"container-title":["ACM Transactions on Intelligent Systems and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3582561","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3582561","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:47:16Z","timestamp":1750178836000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3582561"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,13]]},"references-count":44,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,6,30]]}},"alternative-id":["10.1145\/3582561"],"URL":"https:\/\/doi.org\/10.1145\/3582561","relation":{},"ISSN":["2157-6904","2157-6912"],"issn-type":[{"value":"2157-6904","type":"print"},{"value":"2157-6912","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,13]]},"assertion":[{"value":"2021-12-05","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-12-27","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-04-13","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}